(1) Field of the Invention
The invention relates to neural networks and is directed more particularly to a spatial image processor neural network for processing spatial image data to distinguish one configuration of component objects from a different configuration of the same component objects.
(2) Description of the Prior Art
Electronic neural networks have been developed to rapidly identify patterns in certain types of input data, or to classify accurately the input patterns into one of a plurality of predetermined classifications. For example, neural networks have been developed which can recognize and identify patterns, such as the identification of hand-written alphanumeric characters, in response to input data constituting the pattern of on/off picture elements, or xe2x80x9cpixels,xe2x80x9d representing the images of the characters to be identified. In such a neural network, the pixel pattern is represented by, for example, electrical signals coupled to a plurality of input terminals, which, in turn, are connected to a number of processing nodes, each of which is associated with one of the alphanumeric characters which the neural network can identify. The input signals from the input terminals are coupled to the processing nodes through certain weighting functions, and each processing node generates an output signal which represents a value that is a non-linear function of the pattern of weighted input signals applied thereto. Based on the values of the weighted pattern of input signals from the input terminals, if the input signals represent a character, which can be identified by the neural network, one of the processing nodes, which is associated with that character will generate a positive output signal, and the others will not. On the other hand, if the input signals do not represent a character, which can be identified by the neural network, none of the processing nodes will generate a positive output signal. Neural networks have been developed which can perform similar pattern recognition in a number of diverse areas.
The particular patterns that the neural network can identify depend on the weighting functions and the particular connections of the input terminals to the processing nodes. As an example, the weighting functions in the above-described character recognition neural network essentially will represent the pixel patterns that define each particular character. Typically, each processing node will perform a summation operation in connection with values representing the weighted input signals provided thereto, to generate a sum that represents the likelihood that the character to be identified is the character associated with that processing node. The processing node then applies the nonlinear function to that sum to generate a positive output signal if the sum is, for example, above a predetermined threshold value. Conventional nonlinear functions which processing nodes may use in connection with the sum of weighted input signals generally include a step function, a threshold function, or a sigmoid. In all cases the output signal from the processing node will approach the same positive output signal asymptotically.
Before a neural network can be useful, the weighting functions for each of the respective input signals must be established. In some cases, the weighting functions can be established a priori. Normally, however, a neural network goes through a training phase in which input signals representing a number of training patterns for the types of items to be classified (e.g., the pixel patterns of the various hand-written characters in the character-recognition example) are applied to the input terminals, and the output signals from the processing nodes are tested. Based on the pattern of output signals from the processing nodes for each training example, the weighting functions are adjusted over a number of trials. After being trained, the neural network can generally accurately recognize patterns during an operational phase, with the degree of success based in part on the number of training patterns applied to the neural network during the training stage, and the degree of dissimilarity between patterns to be identified. Such a neural network can also typically identify patterns that are similar, but not necessarily identical, to the training patterns.
One of the problems with conventional neural network architectures as described above is that the training methodology, generally known as the xe2x80x9cback-propagationxe2x80x9d method, is often extremely slow in a number of important applications. In addition, under the back-propagation method, the neural network may result in erroneous results, which may require restarting of training. Even after a neural network has been through a training phase confidence that the best training has been accomplished may sometimes be poor. If a new classification is to be added to a trained neural network, the complete neural network must be retrained. In addition, the weighting functions generated during the training phase often cannot be interpreted in ways that readily provide understanding of what they particularly represent.
Thus, a neural network is typically considered to be a trainable entity that can be taught to transform information for a purpose. Neural networks are adaptable through a form of training, which is usually by example. Long training times is a problem in trainable neural networks.
The spatial image processor is part of a new neural network technology that is constructed rather than trained as in common neural networks. Since the words xe2x80x9cneural networkxe2x80x9d often connote a totally trainable neural network, the full definition of a constructed neural network, as used herein, is as follows: A constructed neural network is a connectionist neural network system that is assembled using common neural network components to perform a specific process. The assembly is analogous to the construction of an electronic assembly using resistors, transistors, integrated circuits and other simple electronic parts. Some examples of common neural components are specific values and/or types of connections, processing elements (neurons), output functions, gain elements and other artificial neural network parts. As in electronics, the laws of nature, such as mathematics, physics, chemistry, mechanics, and xe2x80x9cRules of Experiencexe2x80x9d govern the assembly and architecture of a constructed neural network. A constructed neural network, which is assembled for a specific process without the necessity of training, can be considered equivalent to a trained common neural network with an infinite training sequence that has attained an output error of zero. Most neural network systems of many constructed neural network modules, such as the spatial objects data fuser, have weights that are never altered after they are constructed. When the traditional neural network system is trained, learning occurs only in special memory modules. Such special memory modules are part of this new constructed neural network technology that learns an example in a single application and does not require a retraining of the old examples when a new example is added to a previously trained system, i.e., old memory is retained and not altered.
In artificial neural networks various neural components have synonyms. For example a xe2x80x9cneuronxe2x80x9d, a xe2x80x9cprocessing elementxe2x80x9d and a xe2x80x9cprocessing nodexe2x80x9d are the same. A xe2x80x9cconnection valuexe2x80x9d, a xe2x80x9cweight valuexe2x80x9d and xe2x80x9cweighting valuexe2x80x9d are the same. One or more of such synonyms are used in this and or other associated applications.
Despite advances in spatial image processors, there remains a need for a spatial image processor neural network wherein the spatial image processor neural network has a very high neuron count (approximately 105 to 108 neurons), depending on the multidimensional space the neural network modules operate, and is of an architectural structure providing unique attributes:
(1) The spatial image processor discriminates between two groups comprised of identical components in two different spatial configurations. It is noted that most all image recognition systems cannot discriminate between two such groups.
(2) The spatial image processor increases its sensitivity or attention to an object of interest in a field of more than one object.
(3) The spatial image processor increases its sensitivity to an object of interest in a field where one or more other objects are of non-interest.
(4) The spatial image processor recognizes a partially hidden object when the object is incomplete or is bisected by one or more other objects.
(5) The spatial image processor recognizes one or more objects in a field of many different objects.
(6) The spatial image processor interfaces with an external neural network or system (not described herein) that uninhibits an object that becomes the spatial image processor""s xe2x80x9cchoicexe2x80x9d of object to be fully recognized and to be attentive of the object when such an object is in or enters the visual field.
(7) The spatial image processor has a prototype output that represents the general class of a recognized object regardless of the status of the external system activations.
(8) The spatial image processor contains a low level of processing outputs that represent peripheral vision recognition outputs. Each of the processed outputs provides an activation for a component object image in any position on the retina.
(9) The spatial image processor recognizes various sizes of the same object. An object, subtending varying size virtual images in the image field, as it is viewed from near to far, is continuously recognized as the same object.
(10) A first embodiment of a spatial image processor xe2x80x9cretinaxe2x80x9d contains a connection set that gives it a natural image position invariant processing retina.
(11) A second embodiment of a spatial image processor xe2x80x9cretinaxe2x80x9d contains a connection set that gives it a natural processing fovea. The foveating (foveal vision) retina contains a xe2x80x9cnatural sweet spot of recognitionxe2x80x9d without an architecture of geometric division to provide this process. It is noted that the general definition of a foveating retina, or foveal vision, has two defining human characteristics. One is that an image seen in bright light is sensed in color as the fovea contains mostly cones. The second is that the fovea contains an area of the eye having a high concentration of photonic elements to produce recognitions with fine detail in contrast to the course detail of peripheral vision. The spatial image processor uses a high resolution monochrome photo transducer through out the retina.
(12) The spatial image processor senses a spatial arrangement of component objects to process a temporal signal containing the spatial data.
(13) The spatial image processor has a high memory efficiency as it can use a component object in more than one high level object of recognition.
(14) The spatial image processor uses linear neurons in most all neural network processings.
(15) The spatial image processor architecture is designed and operates under one or more technologies such as constructed neural network, concurrent multiple frequency band and synchronous nonlinear dynamical (chaos) technologies.
It is, then, an object of the invention to provide a spatial image processor neural network having the desired attributes noted herein immediately above.
With the above and other objects in view, as will hereinafter appear, a feature of the present invention is the provision of a spatial image processor neural network for processing image data to discriminate a first spatial configuration of component objects from a second configuration of identical component objects, the network comprising: a photo transducer input array for converting a virtual image to pixel data and sending a signal indicative of said pixel data; a localized gain network (LGN) module for receiving the signal indicative of the pixel data, wherein each input pixel drives a corresponding neuron, and increasing the gain of individual neurons as a function of attention activations; and a retina array and parallel memory processor for receiving the pixel data from the LGN module, for processing the pixel data into memory vectors and for generating a signal including attention activators for the localized gain network module and synchronous temporal activations. The network further comprises neuron arrays, component recognition vectors and chaotic oscillators (nonlinear dynamical oscillators) assembly for receiving the memory vectors, for receiving associative connection feedback and for sending feedback data to the retina array and parallel memory processor. Each of the component recognition vectors is operable to activate a chaotic oscillator, with each of the chaotic oscillators being different each to represent one of the component objects. The component recognition vectors further send peripheral vision object activations. The network also includes a temporal spatial retina for receiving the pixel data from the localized gain network module and the temporal activations from the component recognition vector assembly and parallel memory processor, for generating temporal spatial data and for sending temporal spatial vectors. Also, a temporal parallel memory processor receives the temporal spatial vectors from the temporal spatial retina and sends temporal component memory vectors. The network still further comprises a temporal, spatial and object recognition vector array for receiving the temporal component memory vector from the temporal retina array and parallel memory processor and external associative connections, for forming an object representation of the first configuration of component objects, for sending prototype object activations and for sending the associative connection feedback to the neuron array, component recognition vectors, and to synchronize chaotic oscillator assemblies, which in turn further increases the attentive signal for feedback to the LGN and temporal spatial retina.
The above and other features of the invention, including various novel details of construction and combinations of parts, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular network arrangement embodying the invention is shown by way of illustration only and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention.